Abstract—

The employment information of university graduates contains a lot of useful information that can provide guidance for employment. This paper studied the decision tree method in data mining and improved C4.5 with Taylor’s median theorem in order to further improve its computational efficiency. The information of the 2021 graduates of China West Normal University was used as an example for analysis. It was found that practical ability, English level, and computer level had a great influence on graduate destination. In addition, compared with the traditional C4.5 algorithm, the improved C4.5 algorithm was much more efficient. The calculation time of the improved C4.5 algorithm was 6.27% shorter than the traditional C4.5 algorithm when analyzing 50 000 data. The improved C4.5 algorithm had an average accuracy of 89.81% when analyzing 200 data. The experimental results demonstrate the reliability of the improved C4.5 algorithm for employment information analysis and its applicability in practical employment management.

中文翻译：

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基于数据挖掘的大学毕业生就业信息分析

摘要-

大学毕业生的就业信息包含了大量可以为就业提供指导的有用信息。本文研究了数据挖掘中的决策树方法，并利用泰勒中值定理对C4.5进行了改进，以进一步提高其计算效率。以西华师范大学2021届毕业生信息为例进行分析。研究发现，实践能力、英语水平、计算机水平对毕业去向影响较大。此外，与传统的C4.5算法相比，改进后的C4.5算法的效率要高得多。在分析5万条数据时，改进的C4.5算法的计算时间比传统C4.5算法缩短了6.27%。改进后的C4.5算法在分析200个数据时平均准确率为89.81%。实验结果验证了改进的C4.5就业信息分析算法的可靠性及其在实际就业管理中的适用性。